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Used #MeToo tweets from Twitter Dataset for analyzing sentiments of MeToo movements and major topics involed. Also clustered tweets on common interest.

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#MeToo Analysis

(If unable to view notebook, Please copy - paste link of notebook on https://nbviewer.jupyter.org/ , Thanks.)

#MeToo spread as a hashtag used on social media in an attempt to showcase the widespread prevalence of sexual assault and harassment, majorly in the workplace. Used #MeToo tweets from Twitter Dataset for analyzing sentiments of MeToo movements and major topics involed, negative tweets associated with the topics etc. Also clustered tweets on common interest.

Analysis Includes:

Sentiments of Tweets : (Valence Aware Dictionary and Sentiment Reasoner) Found :

 1) Negative Sentiments: 36.7%
 2) Neutral Sentiments: 29.5%
 3) Positive Sentiments: 33.8%

Major Topics Involved :

(Latent Dirichlet allocation Topic Modelling)
1) Dylan Farrow - Woody Allen Controversy
2) Movement Expressions 
3) Political Party
4) Trump
5) Hugh Hewwit
6) Corey Lewandowski 
7) Roy Moore & Alabama
8) Time to Speak Up!
9) Types of Harrasments

Clustering tweets on common interests:

1) Found tweets related to common topic
2) Clustering

Number of negative tweets per Topic

Dataset: https://data.world/brigi/metoo/workspace/file?agentid=balexturner&datasetid=390-000-metoo-tweets&filename=metoo_tweets_dec2017.csv

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Used #MeToo tweets from Twitter Dataset for analyzing sentiments of MeToo movements and major topics involed. Also clustered tweets on common interest.

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